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Atmospheric rivers (ARs) deliver significant and essential precipitation to the western United States (US) with consequential interannual variability. The intensity and frequency of ARs strongly influence reservoir levels, mountain snowpack, and groundwater recharge, which are key drivers of water‐resource availability and natural hazards. Between October 2022 and April 2023, western states experienced exceptionally heavy precipitation from several families of powerful ARs. Using observations of surface‐loading deformation from Global Navigation Satellite Systems, we find that terrestrial water‐storage gains exceeded 100% of normal within vital California watersheds. Independent water‐storage solutions derived from different data‐analysis and inversion methods provide an important measure of precision. The sustained storage increases, which we show are closely associated with ARs at daily‐to‐weekly timescales, alleviated both meteorological and hydrological drought conditions in the region, with a lag in hydrological‐drought improvements. Quantifying water‐storage recovery associated with extreme precipitation after drought advances understanding of an increasingly variable hydrologic cycle. Plain Language Summary: Human communities in the western United States rely on seasonal mountain snowpack and precipitation for agriculture, industry, and the civilian water supply. Over the past two decades, increasingly severe drought conditions have plagued western states. In this study, we investigate gains in water storage related to recent atmospheric rivers, which are long and narrow corridors of atmospheric moisture associated with extreme precipitation, using an interdisciplinary approach called hydrogeodesy. Hydrogeodesy uses satellite‐ and ground‐based sensors to track subtle changes in the shape of the Earth and its gravity field caused by the redistribution of water through the Earth system. By treating the Earth as a natural scale, we can infer how much water is gained or lost from a particular area over time. Advancing our understanding of water‐resource availability, the Earth's water cycle, and the Earth's changing climate is critical for improving water‐resource management, sustaining natural ecosystems, and mitigating natural hazards. Key Points: We use surface deformation to assess changes in terrestrial water storage (TWS) associated with families of strong atmospheric rivers (ARs) in 2022–2023Large jumps in mountain water storage from ARs spurred recoveries from major California droughts in 2012–2015 and 2019–2022Early‐season water‐storage gains, driven by ARs, nearly equal precipitation inputs, indicating extensive reservoir recharge [ABSTRACT FROM AUTHOR]

Streamflow predictions are critical for managing water resources and for environmental conservation, especially in the water-short Western United States. Land surface models (LSMs), such as the variable infiltration capacity (VIC) model and the Noah LSM with multiparameterization options (Noah-MP), play an essential role in providing comprehensive runoff predictions across the region. Virtually all LSMs require parameter estimation (calibration) to optimize their predictive capabilities. Here, we focus on the calibration of VIC and Noah-MP models at a 1/16 ° latitude–longitude resolution across the Western United States. We first performed global optimal calibration of parameters for both models for 263 river basins in the region. We find that the calibration significantly improves the models' performance, with the median daily streamflow Kling–Gupta efficiency (KGE) increasing from 0.37 to 0.70 for VIC, and from 0.22 to 0.54 for Noah-MP. In general, post-calibration model performance is higher for watersheds with relatively high precipitation and runoff ratios, and at lower elevations. At a second stage, we regionalize the river basin calibrations using the donor-basin method, which establishes transfer relationships for hydrologically similar basins, via which we extend our calibration parameters to 4816 hydrologic unit code (HUC)-10 basins across the region. Using the regionalized parameters, we show that the models' capabilities to simulate high and low flow conditions are substantially improved following calibration and regionalization. The refined parameter sets we developed are intended to support regional hydrological studies and hydrological assessments of climate change impacts. [ABSTRACT FROM AUTHOR]

Streamflow forecasts are critical for water and environmental management, especially in the water-short Western U.S.. Land Surface Models (LSMs), such as the Variable Infiltration Capacity (VIC) model and the Noah-Multiparameterization (Noah-MP) play an essential role in providing comprehensive runoff forecasts across the region. Virtually all LSMs require parameter estimation to optimize their predictive capabilities. We describe a systematic calibration of parameters for VIC and Noah-MP over 263 river basins in the Western U.S., and distribution of the calibrated parameters over the entire region. Post-calibration results showed a notable improvement in model accuracy in the calibration basins: the median daily streamflow Kling-Gupta Efficiency (KGE) for VIC rose from 0.37 to 0.70, and for Noah-MP, from 0.22 to 0.54. Employing the donor-basin regionalization method, we developed transfer relationships to hydrologically similar basins and extended the calibrated parameters to ungauged basins and the entire region. We assessed factors that influence calibration efficiency and model performance using regional parameter estimates. We evaluated high and low flow simulation capabilities of the two models and observed marked improvements after calibration and regionalization. We also generated gridded parameter sets for both models across all 4816 HUC-10 basins in the Western U.S., a data set that is intended to support regional hydrologic studies and hydrologic climate change assessments. [ABSTRACT FROM AUTHOR]

Soil moisture (SM) spatiotemporal variability critically influences water resources, agriculture, and climate. However, besides site‐specific studies, little is known about how SM varies locally (1–100‐m scale). Consequently, quantifying the SM variability and its impact on the Earth system remains a long‐standing challenge in hydrology. We reveal the striking variability of local‐scale SM across the United States using SMAP‐HydroBlocks — a novel satellite‐based surface SM data set at 30‐m resolution. Results show how the complex interplay of SM with landscape characteristics and hydroclimate is primarily driven by local variations in soil properties. This local‐scale complexity yields a remarkable and unique multi‐scale behavior at each location. However, very little of this complexity persists across spatial scales. Experiments reveal that on average 48% and up to 80% of the SM spatial information is lost at the 1‐km resolution, with complete loss expected at the scale of current state‐of‐the‐art SM monitoring and modeling systems (1–25 km resolution). Plain Language Summary: Soil moisture (SM) widely varies in space and time. This variability critically influences freshwater availability, agriculture, ecosystem dynamics, climate and land‐atmosphere interactions, and it can also trigger hazards such as droughts, floods, landslides, and aggravate wildfires. Limited SM observational data constrained our understanding of this variability and its impact on the Earth system. Here, we present the first continental assessment of how SM varies at the local scales using SMAP‐HydroBlocks – the first 30‐m surface SM data set over the United States. This study maps the SM spatial variability, characterizes the landscape drivers, and quantifies how this variability persists across larger spatial scales. Results revealed striking SM spatial variability across the United States, mainly driven by local spatial variations in soil properties and less so by vegetation and topography. However, this SM variability does not persist at coarser spatial scales resulting in extensive information loss. This information loss implicates inaccuracies when predicting non‐linear SM‐dependent hydrological, ecological, and biogeochemical processes using coarse‐scale models and satellite estimates. By mapping the SM spatial variability locally and its scaling behavior, we provide a pathway toward understanding SM‐dependent hydrological, biogeochemical, and ecological processes at local (and so far unresolved) spatial scales. Key Points: 30‐m soil moisture (SM) data shows striking and complex spatial variability driven mainly by climate and local variations in soil propertiesThis variability yields a remarkable and unique multi‐scale behavior at each location that cannot be generalized across the diverse USUp to 80% of SM information is lost at the 1‐km scale, with complete loss at the scale of state‐of‐the‐art SM observation/monitoring systems [ABSTRACT FROM AUTHOR]

Soil moisture plays a key role in controlling land-atmosphere interactions, with implications for water resources, agriculture, climate, and ecosystem dynamics. Although soil moisture varies strongly across the landscape, current monitoring capabilities are limited to coarse-scale satellite retrievals and a few regional in-situ networks. Here, we introduce SMAP-HydroBlocks (SMAP-HB), a high-resolution satellite-based surface soil moisture dataset at an unprecedented 30-m resolution (2015–2019) across the conterminous United States. SMAP-HB was produced by using a scalable cluster-based merging scheme that combines high-resolution land surface modeling, radiative transfer modeling, machine learning, SMAP satellite microwave data, and in-situ observations. We evaluated the resulting dataset over 1,192 observational sites. SMAP-HB performed substantially better than the current state-of-the-art SMAP products, showing a median temporal correlation of 0.73 ± 0.13 and a median Kling-Gupta Efficiency of 0.52 ± 0.20. The largest benefit of SMAP-HB is, however, the high spatial detail and improved representation of the soil moisture spatial variability and spatial accuracy with respect to SMAP products. The SMAP-HB dataset is available via zenodo and at https://waterai.earth/smaphb. Measurement(s) wetness of soil Technology Type(s) computational modeling technique Factor Type(s) geographic location • temporal interval Sample Characteristic - Environment land • surface soil Sample Characteristic - Location contiguous United States of America Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.14582265 [ABSTRACT FROM AUTHOR]

Conventional basin‐by‐basin approaches to calibrate hydrologic models are limited to gauged basins and typically result in spatially discontinuous parameter fields. Moreover, the consequent low calibration density in space falls seriously behind the need from present‐day applications like high resolution river hydrodynamic modeling. In this study we calibrated three key parameters of the Variable Infiltration Capacity (VIC) model at every 1/8° grid‐cell using machine learning‐based maps of four streamflow characteristics for the conterminous United States (CONUS), with a total of 52,663 grid‐cells. This new calibration approach, as an alternative to parameter regionalization, applied to ungauged regions too. A key difference made here is that we tried to regionalize physical variables (streamflow characteristics) instead of model parameters whose behavior may often be less well understood. The resulting parameter fields no longer presented any spatial discontinuities and the patterns corresponded well with climate characteristics, such as aridity and runoff ratio. The calibrated parameters were evaluated against observed streamflow from 704/648 (calibration/validation period) small‐to‐medium‐sized catchments used to derive the streamflow characteristics, 3941/3809 (calibration/validation period) small‐to‐medium‐sized catchments not used to derive the streamflow characteristics as well as five large basins. Comparisons indicated marked improvements in bias and Nash‐Sutcliffe efficiency. Model performance was still poor in arid and semiarid regions, which is mostly due to both model structural and forcing deficiencies. Although the performance gain was limited by the relative small number of parameters to calibrate, the study and results here served as a proof‐of‐concept for a new promising approach for fine‐scale hydrologic model calibrations. Key Points: Novel method proposed for hydrologic model calibration using machine learning‐based maps of streamflow characteristics1/8° grid‐by‐grid calibration resulting in parameters with good spatial coherenceCalibrated parameters substantially improved model performance in runoff prediction [ABSTRACT FROM AUTHOR]

There is a long history of debate on the usefulness of climate model-based seasonal hydroclimatic forecasts as compared to ensemble streamflow prediction (ESP). In this study, the authors use NCEP's operational forecast system, the Climate Forecast System version 2 (CFSv2), and its previous version, CFSv1, to investigate the value of climate models by conducting a set of 27-yr seasonal hydroclimatic hindcasts over the conterminous United States (CONUS). Through Bayesian downscaling, climate models have higher squared correlation R2 and smaller error than ESP for monthly precipitation, and the forecasts conditional on ENSO have further improvements over southern basins out to 4 months. Verification of streamflow forecasts over 1734 U.S. Geological Survey (USGS) gauges shows that CFSv2 has moderately smaller error than ESP, but all three approaches have limited added skill against climatology beyond 1 month because of overforecasting or underdispersion errors. Using a postprocessor, 60%-70% of probabilistic streamflow forecasts are more skillful than climatology. All three approaches have plausible predictions of soil moisture drought frequency over the central United States out to 6 months, and climate models provide better results over the central and eastern United States. The R2 of drought extent is higher for arid basins and for the forecasts initiated during dry seasons, but significant improvements from CFSv2 occur in different seasons for different basins. The R2 of drought severity accumulated over CONUS is higher during winter, and climate models present added value, especially at long leads. This study indicates that climate models can provide better seasonal hydroclimatic forecasts than ESP through appropriate downscaling procedures, but significant improvements are dependent on the variables, seasons, and regions. [ABSTRACT FROM AUTHOR]

The multiscale autoregressive (MAR) framework was introduced in the last decade to process signals that exhibit multiscale features. It provides the method for identifying the multiscale structure in signals and a filtering procedure, and thus is an efficient way to solve the optimal estimation problem for many high-dimensional dynamic systems. Later, an ensemble version of this multiscale filtering procedure, the ensemble multiscale filter (EnMSF), was developed for estimation systems that rely on Monte Carlo samples, making this technique suitable for a range of applications in geosciences. Following the prototype study that introduced EnMSF, a strategy is devised here to implement the multiscale method in a hydrologic data assimilation system, which runs a land surface model. Assimilation experiments are carried out over the Arkansas–Red River basin, located in the central United States (∼645 000 km2), using the Variable Infiltration Capacity (VIC) model with a computing grid of 1062 pixels. A synthetic data assimilation experiment is performed, driven by meteorological forcing fields downscaled from the ensemble forecasts made by the NOAA/National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFS). The classic full-rank ensemble Kalman filter is used as the benchmark to evaluate the multiscale filter performance, and comparisons are also made with a horizontally uncoupled filter. It is demonstrated that the multiscale filter is able to closely approximate the full-rank solution with a low computational cost (∼1/20 of the full-rank filter) in an experiment in which the top-layer soil moisture is assimilated, whereas the horizontally uncoupled filter fails to approximate the full-rank solution. [ABSTRACT FROM AUTHOR]

This article examines profits from momentum strategies when applied to national stock market indexes. The empirical results based on the stock market indexes of 12 European countries and the United States show significant momentum profits. However, our analysis also suggests that the international momentum effect may simply be an empirical illusion due to the use of overlapping data. Specifically, the international momentum effect disappears when the analysis is conducted on nonoverlapping data. Our analysis shows that the international momentum effect, if exists, is mainly driven by national stock market indexes' return autocovariances. However, we find no or little evidence of significant serial correlations in returns for each of the stock market indexes, thereby leading further support to the finding that international momentum effects may not exist. [ABSTRACT FROM AUTHOR]

A procedure is developed to incorporate equality constraints in Kalman filters, including the ensemble Kalman filter (EnKF), and is referred to as the constrained ensemble Kalman filter (CEnKF). The constraint is carried out as a two-step filtering approach, with the first step being the standard (ensemble) Kalman filter. The second step is the constraint step carried out by another Kalman filter that optimally redistributes any imbalance from the first step. The CEnKF is implemented over a 75 000 km² domain in the southern Great Plains region of the United States, using the terrestrial water balance as the constraint. The observations, consisting of gridded fields of the upper two soil moisture layers from the Oklahoma Mesonet system, Atmospheric Radiation Measurement Program Cloud and Radiation Testbed (ARM-CART) energy balance Bowen ratio (EBBR) latent heat estimates, and U.S. Geological Survey (USGS) streamflow from unregulated basins, are assimilated into the Variable Infiltration Capacity (VIC) land surface model. The water balance was applied at the domain scale, and estimates of the water balance components for the domain are updated from the data assimilation step so as to assure closure. [ABSTRACT FROM AUTHOR]

The study examines the aggregate dividend behavior of U.S. corporations based on the permanent earnings hypothesis. Using annual data of aggregate earnings and dividends from 1871-1993, I find that although managers change dividends proportional to permanent earnings changes, they make revisions with a larger percentage change in dividends than in permanent earnings. The results from the post-war data show that firms follow a partial adjustment policy with a long-term dividend payout target in mind and make revisions with a delay. The quarterly data analysis yields results similar to those of the post-war annual data. [ABSTRACT FROM AUTHOR]

This study examines the short- and long-term dependence in the United States and 21 international equity market indexes. Two heteroscedastic-robust testing methods, the modified rescaled range analysis and the rescaled variance ratio test, are employed to test for the existence of dependence. The evidence consistently reveals the absence of long-term dependence in these 22 stock returns indexes. The random walk hypothesis for most, but not all, stock returns indexes is not rejected. When the random walk hypothesis is rejected, the evidence supporting the rejection is weak and the stochastic dependence occurs mainly in short-horizon, rather then long-horizon holding period returns. [ABSTRACT FROM AUTHOR]

In this paper, we examine the nature of transmission of stock returns and volatility between the U.S. and Japanese stock markets using futures prices on the S&P 500 and Nikkei 225 stock indexes. We use stock index futures prices to mitigate the stale quote problem found in the spot index prices and to obtain more robust results. By employing a two-step GARCH approach, we find that there are unidirectional contemporaneous return and volatility spillovers from the U.S. to Japan. Furthermore, the U.S.'s influence on Japan in returns is approximately four times as large as the other way around. Finally, our results show no significant lagged spillover effects in both returns and volatility from the Osaka market to the Chicago market, while a significant lagged volatility spillover is observed from the U.S. to Japan. [ABSTRACT FROM AUTHOR]

Accurate and detailed soil moisture information is essential for, among other things, irrigation, drought and flood prediction, water resources management, and field-scale (i.e., tens of m) decision making. Recent satellite missions measuring soil moisture from space continue to improve the availability of soil moisture information. However, the utility of these satellite products is limited by the large footprint of the microwave sensors. This study presents a merging framework that combines a hyper-resolution land surface model (LSM), a radiative transfer model (RTM), and a Bayesian scheme to merge and downscale coarse resolution remotely sensed hydrological variables to a 30-m spatial resolution. The framework is based on HydroBlocks, an LSM that solves the field-scale spatial heterogeneity of land surface processes through interacting hydrologic response units (HRUs). The framework was demonstrated for soil moisture by coupling HydroBlocks with the Tau-Omega RTM used in the Soil Moisture Active Passive (SMAP) mission. The brightness temperature from the HydroBlocks-RTM and SMAP L3 were merged to obtain updated 30-m soil moisture. We validated the downscaled soil moisture estimates at four experimental watersheds with dense in-situ soil moisture networks in the United States and obtained overall high correlations (> 0.81) and good mean KGE score (0.56). The downscaled product captures the spatial and temporal soil moisture dynamics better than SMAP L3 and L4 product alone at both field and watershed scales. Our results highlight the value of hyper-resolution modeling to bridge the gap between coarse-scale satellite retrievals and field-scale hydrological applications. • Hyper-resolution land surface model improves field-scale soil moisture estimates • Hyper-resolution heterogeneity leverages the soil moisture spatial variability • HRUs allow for computationally efficient merging of remote sensing observations • The merging skill is sensitive to biases in the model and satellite estimates [ABSTRACT FROM AUTHOR]

• A high-salt diet induces hypertension, which eventually leads to cardiovascular remodeling. • High-salt induced hypertensive mice have obvious oxidative stress and endothelial dysfunction. • As a prescription that has been used in clinical practice for many years,Xin-Ji-Er-Kanghas the function of antioxidant and balance endothelin system. • XJEK mitigates cardiac remodeling in high-salt-induced hypertensive mice. Xin-Ji-Er-Kang (XJEK) is a Chinese herbal formula that has beenreported to exert effective protection against cardiovascular diseases, such as hypertension and myocarditis. The aim of the present study was to investigate the effect of XJEK on high-salt-induced hypertensive mice and its possible mechanism. The model of hypertension was established through a high-salt diet. Sixty male Kunming mice were randomized into six groups, namely the Control, Model, Low-dose XJEK, Middle-dose XJEK, High-dose XJEK and Fosinopril groups (n= 10 per group). Different steady interventions were given to each group: 0.9% Sodium chloride was added to the diet of the Control group and 8% sodium chloride to the diet of the other five groups from the very beginning. An additional 4, 8 and 12 g/kg/day XJEK were intragastrically administered to the Low-dose, Middle-dose and High-dose XJEK groups, respectively, and 2 mg/kg/day fosinopril to the fosinopril group, from the start of week 5. Systolic blood pressure (SBP) was measured weekly from weeks 1 to 8 using the tail-cuff method. At the end of week 8, left ventricular (LV) systolic pressure, LV end-diastolic pressure and rate of rise of LV pressure were examined using a TransonicScisense catheter (Transonic Systems Inc,Ithaca, NY,USA). Endothelium-dependent relaxations induced by acetylcholine were observed in an isolated thoracic aorta ring. Serum and heartsweresampled for the measurement of the following indexes:Serum nitric oxide (NO), superoxide dismutase (SOD) activity and malondialdehyde (MDA) content (determined by colorimetricanalysis); serum angiotensin II(Ang II), endothelin-1, endothelial NO synthase (eNOS), asymmetric dimethylarginine (ADMA), tetrahydrobiopterin (BH 4) concentration and l -arginine (determined by enzyme-linked immunosorbent assay); heart to body weight (HW/BW) ratio; myocardial morphological change (determined by HE and VG staining); myocardial eNOS expression (determined by immunofluorescence), and myocardial endothelin receptor A (ET A) expression (determined by western blotting). Results:Statistical data showed that the HW/BW ratio was significantly decreased in the drug treatment group. XJEK treatment could improve the heart systolic and diastolic function and ameliorate hemodynamic parameters and vascular remodeling indexes. Colorimetric results showed that, compared with the model group, XJEK increased serum SOD, NOlevels, and decreased those of serum MDA and Ang II. XJEK reverted changes in cardiac pathology, decreased the myocardial cross-sectional area, collagen volume fraction and perivascular collagen area and improved endothelial dysfunction (ED) by promoting eNOS activity, enhancing NO bioavailability, increasing the expression of BH 4 and decreasing ET A content. In addition, treatment with XJEK decreased ADMA content in the myocardium. Conclusion:In conclusion, XJEK mitigates cardiac remodeling in high-salt-induced hypertensive mice. The potential mechanism involves improved oxidative stress and endothelial dysfunction, independently of ameliorating BP. [ABSTRACT FROM AUTHOR]